Entropy-based optimisation for binary detection networks
Résumé
This contribution deals with the binary detection networks optimization using an entropy based criterion. The optimization of a detection elementary component consists in applying a variable threshold on the likelihood ratio, which depends on a posteriori probabilities. A gradient algorithm is proposed in order to find this threshold. The optimization results of the detection elementary component using entropy and Bayes' criteria are compared: the proposed approach has a very interesting property of robustness with respect to rare events, or with respect to events for which a priori probabilities are uncertain. In particular, the obtained ROC curve does not recede from the ideal point.